Building AI Applicat... • 9m
How, No Way. LLM is the engine model, once trained and it's ready & done. it has cut off of knowledge. Now these models r the base knowledge(brain). now anyone can wrap it around niche product. Like can perform techniques like RAG & Fine Tuning to model the LLM for non trained data. I think we have use cases.
AI Deep Explorer | f... • 3m
"A Survey on Post-Training of Large Language Models" This paper systematically categorizes post-training into five major paradigms: 1. Fine-Tuning 2. Alignment 3. Reasoning Enhancement 4. Efficiency Optimization 5. Integration & Adaptation 1️⃣ Fin
See MoreA billion dollar dre... • 5m
Turn ANY Website into LLM Knowledge in SECONDS 🤯 Tired of LLMs with limited knowledge? 🧠 This video shows you how to easily scrape any website and turn it into a powerful knowledge base for your own custom AI agents. 🤖 We'll explore: * Crawl for
See MoreStartups | AI | info... • 1m
India's biggest AI startup, $1B Sarvam, just launched its flagship LLM. It's a 24B Mistral small post trained on Indic data with a mere 23 downloads 2 days after launch. In contrast, 2 Korean college trained an open-source model that did ~200k last
See MoreSoftware Engineer | ... • 6m
💡 5 Things You Need to Master for learn for integrating AI into your project 1️⃣ Retrieval-Augmented Generation (RAG): Combine search with AI for precise and context-aware outputs. 2️⃣ Vector Databases: Learn how to store and query embeddings for e
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